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Segmentation of Natural Images with K-Means and Hierarchical Algorithm based on Mixture of Pearson Distributions


Affiliations
1 Department of Computer Science and Engineering, GITAM Deemed to be University, Visakhapatnam 530 045, India
2 Department of Computer Science and Engineering Vignan’s Institute of Information Technology (A), Visakhapatnam 530 049, AP, India
3 Department of Computer Science and Engineering, Koneru Laksmaiah Education Foundation, Greenfield, Vaddeswaram, Guntur 522 502, India
4 Glocal Campus, Konkuk University, Chungju-si Chungcheongbuk-do, 27 478, India

In this paper, an attempt has been made to analyze the performance of the image segmented algorithms with the addition of the Pearsonian Type III mixture model. By using the Type III Pearsonian system of distributions the image segmentation process was carried out in the current article which is a novel technique. With the help of K-component combination of Pearsonian Type III distribution, it is considered that the whole input images are characterized. The performance parameters PRI (Probabilistic Rand Index), GCE (Global Consistency Error) and VOI (Volume of Interest) for the currently considered model are estimated with the help of EM (Expectation Maximization) algorithm. For analyzing the proposed model’s performance, four random images are selected as input for the current model from Berkeley image database. The performance metric parameters PRI, GCE and VOI values given the results as the currently proposed method is providing more précise results for the input images where the regions of the input images selected are with tiles having long upper model and the left skewed images. By the help of image quality measures, the proposed method is performing well for the purpose of retrieving the images with respect to the picture segmenting process which is based on GMM (Gaussian Mixture Model). The current model performance was compared with the other existing models like the k-means hierarchical clustering model and the 3-paprameter regression models.
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  • Segmentation of Natural Images with K-Means and Hierarchical Algorithm based on Mixture of Pearson Distributions

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Authors

P Chandra Sekhar
Department of Computer Science and Engineering, GITAM Deemed to be University, Visakhapatnam 530 045, India
N Thirupathi Rao
Department of Computer Science and Engineering Vignan’s Institute of Information Technology (A), Visakhapatnam 530 049, AP, India
Debnath Bhattacharyya
Department of Computer Science and Engineering, Koneru Laksmaiah Education Foundation, Greenfield, Vaddeswaram, Guntur 522 502, India
Tai-hoon Kim
Glocal Campus, Konkuk University, Chungju-si Chungcheongbuk-do, 27 478, India

Abstract


In this paper, an attempt has been made to analyze the performance of the image segmented algorithms with the addition of the Pearsonian Type III mixture model. By using the Type III Pearsonian system of distributions the image segmentation process was carried out in the current article which is a novel technique. With the help of K-component combination of Pearsonian Type III distribution, it is considered that the whole input images are characterized. The performance parameters PRI (Probabilistic Rand Index), GCE (Global Consistency Error) and VOI (Volume of Interest) for the currently considered model are estimated with the help of EM (Expectation Maximization) algorithm. For analyzing the proposed model’s performance, four random images are selected as input for the current model from Berkeley image database. The performance metric parameters PRI, GCE and VOI values given the results as the currently proposed method is providing more précise results for the input images where the regions of the input images selected are with tiles having long upper model and the left skewed images. By the help of image quality measures, the proposed method is performing well for the purpose of retrieving the images with respect to the picture segmenting process which is based on GMM (Gaussian Mixture Model). The current model performance was compared with the other existing models like the k-means hierarchical clustering model and the 3-paprameter regression models.